Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not straightforward especially when their number is large. Ideally, the ultimate user would like to use these rules to decide which actions to undertake. In the literature, this notion is usually referred to as actionability. We propose a new framework to address actionability. Our goal is to lighten the burden of analyzing a large set of classification rules when the user is confronted to an ?unsatisfactory situation? and needs help to decide about the appropriate actions to remedy to this situation. The method consists in comparing the situation to a set of classification rules. For this purpose, we propose   a new framework for learning action recommendations dealing with complex notions of feasibility and quality of actions. Our approach has been motivated by an environmental application aiming at building a tool to help specialists in charge of the management of a catchment to preserve stream-water quality. The results show the utility of this methodology with regard to enhancing the actionability of a set of classification rules in a real-world application.

The deficiencies of keeping population diversity, prematurity and low success rate of searching the global optimal solution are the shortcomings of genetic algorithm (GA). Based on the bias of samples in the uniform design sampling (UDS) point set, the crossover operation in GA is redesigned. Using the concentrations of antibodies in artificial immune system (AIS), the chromosomes concentration in GA is defined and the clonal selection strategy is designed. In order to solve the maximum clique problem (MCP), an new immune GA (UIGA) is presented based on the clonal selection strategy and UDS. The simulation results show that the UIGA provides superior solution quality, convergence rate, and other various indices to those of the simple and good point GA when solving MCPs.

An extended susceptible-infective (SI) epidemic model is presented in this paper to describe the collective blogging behavior on popular incidental topics. Our model has two major extensions over the classic SI model: in the new model, different blog writers get interested in a specific topic with different probabilities, while in a classic SI model, the infection probability of a disease between any two individuals is identical; the new model takes into consideration the impact of external mainstream media on blog writers, while in a classical SI model, spreading of diseases is merely based on personal contacts between individuals. The new model is capable of explaining the widely observed early burst and heavy tail of topic propagation velocity. The proposed model has a closed-form solution when the individual interest is of uniform distribution with the external influence assumed constant. We validate the proposed model using ten topics from two different data sets: Sina Blog and LiveJournal Blogspace, the results indicating that our model fits the topic propagation velocity and predicts the propagation trend very well.

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance by simulation, using real sensor data streams. Our results demonstrate that our approach is accurate and imposes reasonable communication and power consumption demands.

Traditional learning-to-rank problem mainly focuses on one single type of objects. However, with the rapid growth of the Web 2.0, ranking over multiple interrelated and heterogeneous objects becomes a common situation, e.g., the heterogeneous academic network. In this scenario, one may have much training data for some type of objects (e.g. conferences) while only very few for the interested types of objects (e.g. authors). Thus, the two important questions are: (1) Given a networked data set, how could one borrow supervision from other types of objects in order to build an accurate ranking model for the interested objects with insufficient supervision? (2) If there are links between different objects, how can we exploit their relationships for improved ranking performance? In this work, we first propose a regularized framework called HCDRank to simultaneously minimize two loss functions related to these two domains. Then, we extend the approach by exploiting the link information between heterogeneous objects. We conduct a theoretical analysis to the proposed approach and derive its generalization bound to demonstrate how the two related domains could help each other in learning ranking functions. Experimental results on three different genres of data sets demonstrate the effectiveness of the proposed approaches.